It has been shown that even if all coflows arrive simultaneously and
their sizes are known in advance, the coflow scheduling problem of
single-stage jobs is NP-hard [1]}. Therefore, the coflow
scheduling problem of online multi-stage jobs is more challenging.
In this paper, we propose an Attention-based Reinforcement Learning
Model to generate coflow scheduling policies for multi-stage jobs,
which can process DAGs of arbitrary sizes and shape while ensuring
the accuracy of the scheduling strategy. Considering that jobs from
different applications have different importance, jobs with larger
weights should be given priority. In this paper, we take average/total
weighted JCT as the main optimization goal.
